Computer predictive models have been used for many years in a diverse number of areas, such as in the financial industry. However current approaches have difficulty in providing an automated or semi-automated mechanism for determining whether a suspicious activity, such as credit card fraud, may have occurred. As an illustration, previous systems experience problems in creating predictive models for predicting fraudulent activity.
In accordance with the teachings provided herein, systems and methods for operation upon data processing devices are provided for performing fraud detection. As an example, a system and method can be configured to build a set of predictive models to predict credit card or debit card fraud. A first predictive model is trained using a set of training data. A partitioning criterion is used to determine how to partition the training data into partitions, and is based upon a fraud ranking violation metric. Another predictive model is trained using at least one of the partitions of training data in order to generate a second predictive model. The predictive models are combined for use in predicting credit card or debit card fraud.